Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations100000
Missing cells243984
Missing cells (%)13.6%
Duplicate rows9384
Duplicate rows (%)9.4%
Total size in memory48.9 MiB
Average record size in memory512.6 B

Variable types

Categorical9
Numeric7
Unsupported2

Alerts

Dataset has 9384 (9.4%) duplicate rowsDuplicates
adjusted_total_usd is highly overall correlated with base_salary and 2 other fieldsHigh correlation
base_salary is highly overall correlated with adjusted_total_usd and 3 other fieldsHigh correlation
conversion_rate is highly overall correlated with currencyHigh correlation
currency is highly overall correlated with conversion_rateHigh correlation
employment_type is highly overall correlated with adjusted_total_usd and 1 other fieldsHigh correlation
experience_level is highly overall correlated with adjusted_total_usd and 2 other fieldsHigh correlation
salary_in_usd is highly overall correlated with total_salaryHigh correlation
total_salary is highly overall correlated with base_salary and 2 other fieldsHigh correlation
experience_level has 20000 (20.0%) missing values Missing
employment_type has 23984 (24.0%) missing values Missing
education has 100000 (100.0%) missing values Missing
skills has 100000 (100.0%) missing values Missing
education is an unsupported type, check if it needs cleaning or further analysis Unsupported
skills is an unsupported type, check if it needs cleaning or further analysis Unsupported
years_experience has 4664 (4.7%) zeros Zeros

Reproduction

Analysis started2025-07-06 08:29:54.533558
Analysis finished2025-07-06 08:30:20.246330
Duration25.71 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

job_title
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
Data Analyst
16857 
DevOps Engineer
16764 
Research Scientist
16552 
Sofware Engneer
5755 
Software Engr
5622 
Other values (7)
38450 

Length

Max length21
Median length18
Mean length14.09439
Min length7

Characters and Unicode

Total characters1409439
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData Analyst
2nd rowDevOps Engineer
3rd rowResearch Scientist
4th rowSoftware Engr
5th rowSoftware Engr

Common Values

ValueCountFrequency (%)
Data Analyst 16857
16.9%
DevOps Engineer 16764
16.8%
Research Scientist 16552
16.6%
Sofware Engneer 5755
 
5.8%
Software Engr 5622
 
5.6%
Dt Scientist 5574
 
5.6%
Softwre Engineer 5574
 
5.6%
Data Scienist 5564
 
5.6%
ML Engr 5512
 
5.5%
Data Scntist 5499
 
5.5%
Other values (2) 10727
10.7%

Length

2025-07-06T14:00:20.516809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data 27920
13.6%
engineer 22338
10.9%
scientist 22126
10.8%
analyst 16857
 
8.2%
devops 16764
 
8.2%
research 16552
 
8.1%
engr 16416
 
8.0%
ml 10957
 
5.3%
engneer 5755
 
2.8%
sofware 5755
 
2.8%
Other values (8) 43842
21.4%

Most occurring characters

ValueCountFrequency (%)
e 166704
11.8%
n 149384
10.6%
t 122361
 
8.7%
a 111190
 
7.9%
105282
 
7.5%
i 99226
 
7.0%
r 88739
 
6.3%
s 83362
 
5.9%
g 55236
 
3.9%
c 55023
 
3.9%
Other values (16) 372932
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1409439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 166704
11.8%
n 149384
10.6%
t 122361
 
8.7%
a 111190
 
7.9%
105282
 
7.5%
i 99226
 
7.0%
r 88739
 
6.3%
s 83362
 
5.9%
g 55236
 
3.9%
c 55023
 
3.9%
Other values (16) 372932
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1409439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 166704
11.8%
n 149384
10.6%
t 122361
 
8.7%
a 111190
 
7.9%
105282
 
7.5%
i 99226
 
7.0%
r 88739
 
6.3%
s 83362
 
5.9%
g 55236
 
3.9%
c 55023
 
3.9%
Other values (16) 372932
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1409439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 166704
11.8%
n 149384
10.6%
t 122361
 
8.7%
a 111190
 
7.9%
105282
 
7.5%
i 99226
 
7.0%
r 88739
 
6.3%
s 83362
 
5.9%
g 55236
 
3.9%
c 55023
 
3.9%
Other values (16) 372932
26.5%

experience_level
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing20000
Missing (%)20.0%
Memory size5.9 MiB
Mid
20079 
Senior
20063 
Lead
19944 
Junior
19914 

Length

Max length6
Median length4
Mean length4.7484375
Min length3

Characters and Unicode

Total characters379875
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid
2nd rowMid
3rd rowLead
4th rowLead
5th rowLead

Common Values

ValueCountFrequency (%)
Mid 20079
20.1%
Senior 20063
20.1%
Lead 19944
19.9%
Junior 19914
19.9%
(Missing) 20000
20.0%

Length

2025-07-06T14:00:20.909687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:21.197897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mid 20079
25.1%
senior 20063
25.1%
lead 19944
24.9%
junior 19914
24.9%

Most occurring characters

ValueCountFrequency (%)
i 60056
15.8%
d 40023
10.5%
e 40007
10.5%
n 39977
10.5%
o 39977
10.5%
r 39977
10.5%
M 20079
 
5.3%
S 20063
 
5.3%
L 19944
 
5.3%
a 19944
 
5.3%
Other values (2) 39828
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 379875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 60056
15.8%
d 40023
10.5%
e 40007
10.5%
n 39977
10.5%
o 39977
10.5%
r 39977
10.5%
M 20079
 
5.3%
S 20063
 
5.3%
L 19944
 
5.3%
a 19944
 
5.3%
Other values (2) 39828
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 379875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 60056
15.8%
d 40023
10.5%
e 40007
10.5%
n 39977
10.5%
o 39977
10.5%
r 39977
10.5%
M 20079
 
5.3%
S 20063
 
5.3%
L 19944
 
5.3%
a 19944
 
5.3%
Other values (2) 39828
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 379875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 60056
15.8%
d 40023
10.5%
e 40007
10.5%
n 39977
10.5%
o 39977
10.5%
r 39977
10.5%
M 20079
 
5.3%
S 20063
 
5.3%
L 19944
 
5.3%
a 19944
 
5.3%
Other values (2) 39828
10.5%

employment_type
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing23984
Missing (%)24.0%
Memory size6.2 MiB
Part-time
19146 
Full-time
19129 
Contract
18901 
Intern
18840 

Length

Max length9
Median length9
Mean length8.0078273
Min length6

Characters and Unicode

Total characters608723
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContract
2nd rowContract
3rd rowFull-time
4th rowIntern
5th rowContract

Common Values

ValueCountFrequency (%)
Part-time 19146
19.1%
Full-time 19129
19.1%
Contract 18901
18.9%
Intern 18840
18.8%
(Missing) 23984
24.0%

Length

2025-07-06T14:00:21.610399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:22.360015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
part-time 19146
25.2%
full-time 19129
25.2%
contract 18901
24.9%
intern 18840
24.8%

Most occurring characters

ValueCountFrequency (%)
t 114063
18.7%
e 57115
9.4%
r 56887
9.3%
n 56581
9.3%
i 38275
 
6.3%
m 38275
 
6.3%
- 38275
 
6.3%
l 38258
 
6.3%
a 38047
 
6.3%
P 19146
 
3.1%
Other values (6) 113801
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 114063
18.7%
e 57115
9.4%
r 56887
9.3%
n 56581
9.3%
i 38275
 
6.3%
m 38275
 
6.3%
- 38275
 
6.3%
l 38258
 
6.3%
a 38047
 
6.3%
P 19146
 
3.1%
Other values (6) 113801
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 114063
18.7%
e 57115
9.4%
r 56887
9.3%
n 56581
9.3%
i 38275
 
6.3%
m 38275
 
6.3%
- 38275
 
6.3%
l 38258
 
6.3%
a 38047
 
6.3%
P 19146
 
3.1%
Other values (6) 113801
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 114063
18.7%
e 57115
9.4%
r 56887
9.3%
n 56581
9.3%
i 38275
 
6.3%
m 38275
 
6.3%
- 38275
 
6.3%
l 38258
 
6.3%
a 38047
 
6.3%
P 19146
 
3.1%
Other values (6) 113801
18.7%

company_size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
Small
33538 
Medium
33469 
Large
32993 

Length

Max length6
Median length5
Mean length5.33469
Min length5

Characters and Unicode

Total characters533469
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowSmall
3rd rowMedium
4th rowLarge
5th rowLarge

Common Values

ValueCountFrequency (%)
Small 33538
33.5%
Medium 33469
33.5%
Large 32993
33.0%

Length

2025-07-06T14:00:22.747619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:23.030235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
small 33538
33.5%
medium 33469
33.5%
large 32993
33.0%

Most occurring characters

ValueCountFrequency (%)
l 67076
12.6%
m 67007
12.6%
a 66531
12.5%
e 66462
12.5%
S 33538
6.3%
M 33469
6.3%
d 33469
6.3%
i 33469
6.3%
u 33469
6.3%
L 32993
6.2%
Other values (2) 65986
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 533469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 67076
12.6%
m 67007
12.6%
a 66531
12.5%
e 66462
12.5%
S 33538
6.3%
M 33469
6.3%
d 33469
6.3%
i 33469
6.3%
u 33469
6.3%
L 32993
6.2%
Other values (2) 65986
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 533469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 67076
12.6%
m 67007
12.6%
a 66531
12.5%
e 66462
12.5%
S 33538
6.3%
M 33469
6.3%
d 33469
6.3%
i 33469
6.3%
u 33469
6.3%
L 32993
6.2%
Other values (2) 65986
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 533469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 67076
12.6%
m 67007
12.6%
a 66531
12.5%
e 66462
12.5%
S 33538
6.3%
M 33469
6.3%
d 33469
6.3%
i 33469
6.3%
u 33469
6.3%
L 32993
6.2%
Other values (2) 65986
12.4%

company_location
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
UK
17087 
Remote
16704 
USA
16630 
Canada
16558 
India
16511 

Length

Max length7
Median length6
Mean length4.81761
Min length2

Characters and Unicode

Total characters481761
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowIndia
3rd rowGermany
4th rowIndia
5th rowGermany

Common Values

ValueCountFrequency (%)
UK 17087
17.1%
Remote 16704
16.7%
USA 16630
16.6%
Canada 16558
16.6%
India 16511
16.5%
Germany 16510
16.5%

Length

2025-07-06T14:00:23.376369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:23.726810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
uk 17087
17.1%
remote 16704
16.7%
usa 16630
16.6%
canada 16558
16.6%
india 16511
16.5%
germany 16510
16.5%

Most occurring characters

ValueCountFrequency (%)
a 82695
17.2%
e 49918
 
10.4%
n 49579
 
10.3%
U 33717
 
7.0%
m 33214
 
6.9%
d 33069
 
6.9%
K 17087
 
3.5%
t 16704
 
3.5%
R 16704
 
3.5%
o 16704
 
3.5%
Other values (8) 132370
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 481761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 82695
17.2%
e 49918
 
10.4%
n 49579
 
10.3%
U 33717
 
7.0%
m 33214
 
6.9%
d 33069
 
6.9%
K 17087
 
3.5%
t 16704
 
3.5%
R 16704
 
3.5%
o 16704
 
3.5%
Other values (8) 132370
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 481761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 82695
17.2%
e 49918
 
10.4%
n 49579
 
10.3%
U 33717
 
7.0%
m 33214
 
6.9%
d 33069
 
6.9%
K 17087
 
3.5%
t 16704
 
3.5%
R 16704
 
3.5%
o 16704
 
3.5%
Other values (8) 132370
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 481761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 82695
17.2%
e 49918
 
10.4%
n 49579
 
10.3%
U 33717
 
7.0%
m 33214
 
6.9%
d 33069
 
6.9%
K 17087
 
3.5%
t 16704
 
3.5%
R 16704
 
3.5%
o 16704
 
3.5%
Other values (8) 132370
27.5%

remote_ratio
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
50
33652 
0
33274 
100
33074 

Length

Max length3
Median length2
Mean length1.998
Min length1

Characters and Unicode

Total characters199800
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row100
3rd row0
4th row50
5th row100

Common Values

ValueCountFrequency (%)
50 33652
33.7%
0 33274
33.3%
100 33074
33.1%

Length

2025-07-06T14:00:24.252512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:24.574055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
50 33652
33.7%
0 33274
33.3%
100 33074
33.1%

Most occurring characters

ValueCountFrequency (%)
0 133074
66.6%
5 33652
 
16.8%
1 33074
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 133074
66.6%
5 33652
 
16.8%
1 33074
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 133074
66.6%
5 33652
 
16.8%
1 33074
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 133074
66.6%
5 33652
 
16.8%
1 33074
 
16.6%

salary_currency
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
GBP
20197 
USD
20100 
EUR
20006 
INR
19864 
CAD
19833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINR
2nd rowGBP
3rd rowEUR
4th rowINR
5th rowINR

Common Values

ValueCountFrequency (%)
GBP 20197
20.2%
USD 20100
20.1%
EUR 20006
20.0%
INR 19864
19.9%
CAD 19833
19.8%

Length

2025-07-06T14:00:24.896963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:25.193659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gbp 20197
20.2%
usd 20100
20.1%
eur 20006
20.0%
inr 19864
19.9%
cad 19833
19.8%

Most occurring characters

ValueCountFrequency (%)
U 40106
13.4%
D 39933
13.3%
R 39870
13.3%
G 20197
6.7%
P 20197
6.7%
B 20197
6.7%
S 20100
6.7%
E 20006
6.7%
I 19864
6.6%
N 19864
6.6%
Other values (2) 39666
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 40106
13.4%
D 39933
13.3%
R 39870
13.3%
G 20197
6.7%
P 20197
6.7%
B 20197
6.7%
S 20100
6.7%
E 20006
6.7%
I 19864
6.6%
N 19864
6.6%
Other values (2) 39666
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 40106
13.4%
D 39933
13.3%
R 39870
13.3%
G 20197
6.7%
P 20197
6.7%
B 20197
6.7%
S 20100
6.7%
E 20006
6.7%
I 19864
6.6%
N 19864
6.6%
Other values (2) 39666
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 40106
13.4%
D 39933
13.3%
R 39870
13.3%
G 20197
6.7%
P 20197
6.7%
B 20197
6.7%
S 20100
6.7%
E 20006
6.7%
I 19864
6.6%
N 19864
6.6%
Other values (2) 39666
13.2%

years_experience
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01073
Minimum0
Maximum20
Zeros4664
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:25.593803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0580824
Coefficient of variation (CV)0.6051589
Kurtosis-1.2126856
Mean10.01073
Median Absolute Deviation (MAD)5
Skewness-0.0072859482
Sum1001073
Variance36.700362
MonotonicityNot monotonic
2025-07-06T14:00:25.999389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 4986
 
5.0%
1 4953
 
5.0%
3 4944
 
4.9%
15 4943
 
4.9%
13 4891
 
4.9%
12 4856
 
4.9%
17 4849
 
4.8%
9 4847
 
4.8%
6 4827
 
4.8%
20 4794
 
4.8%
Other values (11) 51110
51.1%
ValueCountFrequency (%)
0 4664
4.7%
1 4953
5.0%
2 4620
4.6%
3 4944
4.9%
4 4665
4.7%
5 4774
4.8%
6 4827
4.8%
7 4619
4.6%
8 4655
4.7%
9 4847
4.8%
ValueCountFrequency (%)
20 4794
4.8%
19 4436
4.4%
18 4986
5.0%
17 4849
4.8%
16 4769
4.8%
15 4943
4.9%
14 4764
4.8%
13 4891
4.9%
12 4856
4.9%
11 4575
4.6%

base_salary
Real number (ℝ)

High correlation 

Distinct29893
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273915.48
Minimum-344.33716
Maximum3121412.5
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)< 0.1%
Memory size781.4 KiB
2025-07-06T14:00:26.474193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-344.33716
5-th percentile32282.153
Q159139.57
median92922.921
Q3126053.91
95-th percentile1971418.4
Maximum3121412.5
Range3121756.8
Interquartile range (IQR)66914.345

Descriptive statistics

Standard deviation609824.47
Coefficient of variation (CV)2.2263235
Kurtosis9.6115371
Mean273915.48
Median Absolute Deviation (MAD)33400.224
Skewness3.2565324
Sum2.7391548 × 1010
Variance3.7188588 × 1011
MonotonicityNot monotonic
2025-07-06T14:00:26.966139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2464272.995 691
 
0.7%
2628557.862 685
 
0.7%
2299988.129 679
 
0.7%
985709.1981 679
 
0.7%
1478563.797 678
 
0.7%
1149994.065 675
 
0.7%
2135703.263 670
 
0.7%
2957127.594 667
 
0.7%
2792842.728 662
 
0.7%
1807133.53 662
 
0.7%
Other values (29883) 93252
93.3%
ValueCountFrequency (%)
-344.337158 4
< 0.1%
7114.142105 3
< 0.1%
8012.344674 3
< 0.1%
8309.978998 2
< 0.1%
8414.665655 3
< 0.1%
9408.048384 4
< 0.1%
9530.895562 4
< 0.1%
9595.534428 3
< 0.1%
9691.15952 3
< 0.1%
9788.839675 4
< 0.1%
ValueCountFrequency (%)
3121412.461 644
0.6%
2957127.594 667
0.7%
2792842.728 662
0.7%
2628557.862 685
0.7%
2464272.995 691
0.7%
2299988.129 679
0.7%
2135703.263 670
0.7%
1971418.396 661
0.7%
1807133.53 662
0.7%
1642848.664 650
0.7%

bonus
Real number (ℝ)

Distinct9494
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5336
Minimum0
Maximum9999
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:27.471602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile491
Q12508
median5004
Q37504.25
95-th percentile9514
Maximum9999
Range9999
Interquartile range (IQR)4996.25

Descriptive statistics

Standard deviation2891.5013
Coefficient of variation (CV)0.57823855
Kurtosis-1.1996851
Mean5000.5336
Median Absolute Deviation (MAD)2498
Skewness-0.00068108839
Sum5.0005336 × 108
Variance8360779.7
MonotonicityNot monotonic
2025-07-06T14:00:27.962253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3899 40
 
< 0.1%
6995 39
 
< 0.1%
9583 36
 
< 0.1%
6230 36
 
< 0.1%
5379 35
 
< 0.1%
669 34
 
< 0.1%
217 34
 
< 0.1%
6775 33
 
< 0.1%
4165 33
 
< 0.1%
7140 33
 
< 0.1%
Other values (9484) 99647
99.6%
ValueCountFrequency (%)
0 22
< 0.1%
1 11
< 0.1%
2 14
< 0.1%
3 7
 
< 0.1%
4 16
< 0.1%
5 18
< 0.1%
6 11
< 0.1%
8 12
< 0.1%
9 6
 
< 0.1%
10 21
< 0.1%
ValueCountFrequency (%)
9999 12
< 0.1%
9998 18
< 0.1%
9997 2
 
< 0.1%
9996 7
 
< 0.1%
9995 3
 
< 0.1%
9994 3
 
< 0.1%
9993 15
< 0.1%
9992 14
< 0.1%
9991 13
< 0.1%
9990 23
< 0.1%

stock_options
Real number (ℝ)

Distinct19093
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15014.531
Minimum0
Maximum29998
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:28.437608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1459
Q17463.75
median14995
Q322530
95-th percentile28481
Maximum29998
Range29998
Interquartile range (IQR)15066.25

Descriptive statistics

Standard deviation8664.1427
Coefficient of variation (CV)0.57705051
Kurtosis-1.2025961
Mean15014.531
Median Absolute Deviation (MAD)7533
Skewness-0.0055165875
Sum1.5014531 × 109
Variance75067369
MonotonicityNot monotonic
2025-07-06T14:00:28.941905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10642 28
 
< 0.1%
24318 24
 
< 0.1%
14557 23
 
< 0.1%
14866 22
 
< 0.1%
5233 22
 
< 0.1%
25362 22
 
< 0.1%
20175 21
 
< 0.1%
814 21
 
< 0.1%
27437 21
 
< 0.1%
23970 21
 
< 0.1%
Other values (19083) 99775
99.8%
ValueCountFrequency (%)
0 7
< 0.1%
1 2
 
< 0.1%
6 4
 
< 0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
12 3
 
< 0.1%
14 3
 
< 0.1%
15 3
 
< 0.1%
16 3
 
< 0.1%
17 12
< 0.1%
ValueCountFrequency (%)
29998 2
 
< 0.1%
29997 4
 
< 0.1%
29996 6
< 0.1%
29995 6
< 0.1%
29994 4
 
< 0.1%
29993 11
< 0.1%
29991 6
< 0.1%
29990 6
< 0.1%
29989 4
 
< 0.1%
29984 3
 
< 0.1%

total_salary
Real number (ℝ)

High correlation 

Distinct29976
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105189.4
Minimum13732.471
Maximum196335.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:29.415220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13732.471
5-th percentile48109.539
Q174890.961
median105372.47
Q3135233.16
95-th percentile162065.98
Maximum196335.84
Range182603.37
Interquartile range (IQR)60342.202

Descriptive statistics

Standard deviation36335.187
Coefficient of variation (CV)0.34542631
Kurtosis-1.0074881
Mean105189.4
Median Absolute Deviation (MAD)30156.537
Skewness-0.0069139211
Sum1.051894 × 1010
Variance1.3202458 × 109
MonotonicityNot monotonic
2025-07-06T14:00:29.918492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64334.52187 4
 
< 0.1%
128905.6789 4
 
< 0.1%
106850.9659 4
 
< 0.1%
151561.1 4
 
< 0.1%
134864.555 4
 
< 0.1%
139785.2998 4
 
< 0.1%
151348.7951 4
 
< 0.1%
85420.40669 4
 
< 0.1%
124781.3966 4
 
< 0.1%
44949.2559 4
 
< 0.1%
Other values (29966) 99960
> 99.9%
ValueCountFrequency (%)
13732.47075 3
< 0.1%
15766.39166 3
< 0.1%
16312.83968 4
< 0.1%
17630.64876 3
< 0.1%
17930.1565 3
< 0.1%
18417.2457 2
< 0.1%
18826.42532 4
< 0.1%
18904.66565 3
< 0.1%
19162.07058 3
< 0.1%
19991.32945 2
< 0.1%
ValueCountFrequency (%)
196335.8389 4
< 0.1%
194090.636 4
< 0.1%
193371.2468 4
< 0.1%
191967.2556 2
< 0.1%
191889.8664 4
< 0.1%
189131.5345 3
< 0.1%
188963.1098 4
< 0.1%
188712.7583 4
< 0.1%
188359.1604 3
< 0.1%
188335.4938 4
< 0.1%

salary_in_usd
Real number (ℝ)

High correlation 

Distinct29976
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103339.98
Minimum221.00695
Maximum2354698.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:30.426509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum221.00695
5-th percentile904.53934
Q148338.066
median91291.878
Q3133356.75
95-th percentile190884.52
Maximum2354698.2
Range2354477.2
Interquartile range (IQR)85018.679

Descriptive statistics

Standard deviation146128.71
Coefficient of variation (CV)1.4140579
Kurtosis69.95742
Mean103339.98
Median Absolute Deviation (MAD)42484.123
Skewness7.4038024
Sum1.0333998 × 1010
Variance2.1353601 × 1010
MonotonicityNot monotonic
2025-07-06T14:00:30.904219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83634.87843 4
 
< 0.1%
141796.2468 4
 
< 0.1%
106850.9659 4
 
< 0.1%
1818.7332 4
 
< 0.1%
1483510.105 4
 
< 0.1%
104838.9749 4
 
< 0.1%
113511.5963 4
 
< 0.1%
939624.4735 4
 
< 0.1%
162215.8156 4
 
< 0.1%
5393.910708 4
 
< 0.1%
Other values (29966) 99960
> 99.9%
ValueCountFrequency (%)
221.0069484 2
< 0.1%
239.8959534 2
< 0.1%
290.9995072 3
< 0.1%
319.7131564 4
< 0.1%
320.4255285 2
< 0.1%
326.7661597 4
< 0.1%
332.1927027 4
< 0.1%
332.8976715 3
< 0.1%
334.4453301 4
< 0.1%
349.7129345 2
< 0.1%
ValueCountFrequency (%)
2354698.235 4
< 0.1%
2281888.063 3
< 0.1%
2244156.761 4
< 0.1%
2201626.136 3
< 0.1%
2086524.059 4
< 0.1%
2042707.312 3
< 0.1%
2029208.849 1
 
< 0.1%
1994731.507 4
< 0.1%
1932229.115 2
< 0.1%
1925146.241 3
< 0.1%

currency
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
GBP
20202 
USD
20084 
CAD
20008 
INR
19934 
EUR
19772 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowEUR
3rd rowEUR
4th rowUSD
5th rowINR

Common Values

ValueCountFrequency (%)
GBP 20202
20.2%
USD 20084
20.1%
CAD 20008
20.0%
INR 19934
19.9%
EUR 19772
19.8%

Length

2025-07-06T14:00:31.329312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:31.615753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gbp 20202
20.2%
usd 20084
20.1%
cad 20008
20.0%
inr 19934
19.9%
eur 19772
19.8%

Most occurring characters

ValueCountFrequency (%)
D 40092
13.4%
U 39856
13.3%
R 39706
13.2%
G 20202
6.7%
P 20202
6.7%
B 20202
6.7%
S 20084
6.7%
C 20008
6.7%
A 20008
6.7%
I 19934
6.6%
Other values (2) 39706
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 40092
13.4%
U 39856
13.3%
R 39706
13.2%
G 20202
6.7%
P 20202
6.7%
B 20202
6.7%
S 20084
6.7%
C 20008
6.7%
A 20008
6.7%
I 19934
6.6%
Other values (2) 39706
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 40092
13.4%
U 39856
13.3%
R 39706
13.2%
G 20202
6.7%
P 20202
6.7%
B 20202
6.7%
S 20084
6.7%
C 20008
6.7%
A 20008
6.7%
I 19934
6.6%
Other values (2) 39706
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 40092
13.4%
U 39856
13.3%
R 39706
13.2%
G 20202
6.7%
P 20202
6.7%
B 20202
6.7%
S 20084
6.7%
C 20008
6.7%
A 20008
6.7%
I 19934
6.6%
Other values (2) 39706
13.2%

education
Unsupported

Missing  Rejected  Unsupported 

Missing100000
Missing (%)100.0%
Memory size781.4 KiB

skills
Unsupported

Missing  Rejected  Unsupported 

Missing100000
Missing (%)100.0%
Memory size781.4 KiB

conversion_rate
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
1.3
20202 
1.0
20084 
0.75
20008 
0.012
19934 
1.1
19772 

Length

Max length5
Median length3
Mean length3.59876
Min length3

Characters and Unicode

Total characters359876
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.1
3rd row1.1
4th row1.0
5th row0.012

Common Values

ValueCountFrequency (%)
1.3 20202
20.2%
1.0 20084
20.1%
0.75 20008
20.0%
0.012 19934
19.9%
1.1 19772
19.8%

Length

2025-07-06T14:00:32.072235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T14:00:32.407629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.3 20202
20.2%
1.0 20084
20.1%
0.75 20008
20.0%
0.012 19934
19.9%
1.1 19772
19.8%

Most occurring characters

ValueCountFrequency (%)
. 100000
27.8%
1 99764
27.7%
0 79960
22.2%
3 20202
 
5.6%
7 20008
 
5.6%
5 20008
 
5.6%
2 19934
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 100000
27.8%
1 99764
27.7%
0 79960
22.2%
3 20202
 
5.6%
7 20008
 
5.6%
5 20008
 
5.6%
2 19934
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 100000
27.8%
1 99764
27.7%
0 79960
22.2%
3 20202
 
5.6%
7 20008
 
5.6%
5 20008
 
5.6%
2 19934
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 100000
27.8%
1 99764
27.7%
0 79960
22.2%
3 20202
 
5.6%
7 20008
 
5.6%
5 20008
 
5.6%
2 19934
 
5.5%

adjusted_total_usd
Real number (ℝ)

High correlation 

Distinct81716
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245166.84
Minimum164.78965
Maximum4108339.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-06T14:00:32.863587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum164.78965
5-th percentile934.95383
Q149495.711
median95188.148
Q3143031.64
95-th percentile1664767.8
Maximum4108339.9
Range4108175.1
Interquartile range (IQR)93535.929

Descriptive statistics

Standard deviation591820.23
Coefficient of variation (CV)2.413949
Kurtosis15.302569
Mean245166.84
Median Absolute Deviation (MAD)46891.765
Skewness3.9142544
Sum2.4516684 × 1010
Variance3.5025118 × 1011
MonotonicityNot monotonic
2025-07-06T14:00:33.321553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93003.79188 4
 
< 0.1%
1948.490432 4
 
< 0.1%
80804.65607 4
 
< 0.1%
1318.369372 4
 
< 0.1%
65559.85655 4
 
< 0.1%
102790.098 4
 
< 0.1%
93225.57741 4
 
< 0.1%
97072.98433 4
 
< 0.1%
162614.2464 4
 
< 0.1%
1599.449156 4
 
< 0.1%
Other values (81706) 99960
> 99.9%
ValueCountFrequency (%)
164.7896491 1
< 0.1%
195.7540761 1
< 0.1%
215.161878 1
< 0.1%
221.0069484 1
< 0.1%
225.9171038 1
< 0.1%
226.8559879 1
< 0.1%
229.9448469 1
< 0.1%
239.8959534 1
< 0.1%
248.658584 1
< 0.1%
259.394489 1
< 0.1%
ValueCountFrequency (%)
4108339.899 1
< 0.1%
4106341.799 1
< 0.1%
4105499.399 1
< 0.1%
4105384.999 1
< 0.1%
4104767.499 1
< 0.1%
4104663.499 1
< 0.1%
4104229.299 1
< 0.1%
4103031.999 1
< 0.1%
4102554.899 1
< 0.1%
4102331.299 1
< 0.1%

Interactions

2025-07-06T14:00:14.341380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:04.209974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.700150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.672908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.620801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.513461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:11.514229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:14.732880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:04.475571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.839748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.798002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.753363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.849639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:11.929054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:15.098862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:04.970643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.973325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.933665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.883302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:09.280287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:12.316852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:15.440013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.096593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.109475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.068744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.002905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:09.714611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:12.725527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:15.798737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.302603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.240216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.217432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.133733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:10.171827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:13.151823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:16.187596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.440170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.387133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.376777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.268427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:10.642349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:13.574700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:16.512018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:05.574874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:06.537758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:07.505051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:08.381991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:11.080492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-06T14:00:13.959889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-06T14:00:33.732168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adjusted_total_usdbase_salarybonuscompany_locationcompany_sizeconversion_ratecurrencyemployment_typeexperience_leveljob_titleremote_ratiosalary_currencysalary_in_usdstock_optionstotal_salaryyears_experience
adjusted_total_usd1.0000.6130.0390.0030.0000.1230.1231.0001.0000.0040.0000.0000.2910.1090.4880.003
base_salary0.6131.0000.0080.0000.0090.0040.0041.0001.0000.0010.0050.0000.4630.0050.7870.001
bonus0.0390.0081.0000.0170.0200.0000.0000.0150.0200.0130.0110.0160.046-0.0090.081-0.003
company_location0.0030.0000.0171.0000.0110.0050.0050.0140.0090.0130.0090.0100.0160.0160.0130.016
company_size0.0000.0090.0200.0111.0000.0020.0020.0040.0000.0140.0060.0030.0160.0200.0080.015
conversion_rate0.1230.0040.0000.0050.0021.0001.0000.0030.0000.0000.0000.0030.0050.0000.0000.000
currency0.1230.0040.0000.0050.0021.0001.0000.0030.0000.0000.0000.0030.0050.0000.0000.000
employment_type1.0001.0000.0150.0140.0040.0030.0031.0000.0120.0150.0110.0140.0180.0160.0140.019
experience_level1.0001.0000.0200.0090.0000.0000.0000.0121.0000.0000.0060.0100.0680.0170.7590.014
job_title0.0040.0010.0130.0130.0140.0000.0000.0150.0001.0000.0120.0120.0100.0130.0080.011
remote_ratio0.0000.0050.0110.0090.0060.0000.0000.0110.0060.0121.0000.0090.0150.0100.0110.016
salary_currency0.0000.0000.0160.0100.0030.0030.0030.0140.0100.0120.0091.0000.0500.0110.0180.016
salary_in_usd0.2910.4630.0460.0160.0160.0050.0050.0180.0680.0100.0150.0501.0000.1330.5870.005
stock_options0.1090.005-0.0090.0160.0200.0000.0000.0160.0170.0130.0100.0110.1331.0000.2320.002
total_salary0.4880.7870.0810.0130.0080.0000.0000.0140.7590.0080.0110.0180.5870.2321.0000.004
years_experience0.0030.001-0.0030.0160.0150.0000.0000.0190.0140.0110.0160.0160.0050.0020.0041.000

Missing values

2025-07-06T14:00:17.149625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-06T14:00:18.170810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-06T14:00:19.464754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

job_titleexperience_levelemployment_typecompany_sizecompany_locationremote_ratiosalary_currencyyears_experiencebase_salarybonusstock_optionstotal_salarysalary_in_usdcurrencyeducationskillsconversion_rateadjusted_total_usd
0Data AnalystMidContractMediumGermany0INR1368407.45174711001932588832.4517471065.989421USDNaNNaN1.00088832.451747
1DevOps EngineerMidContractSmallIndia100GBP964193.11777521941916485551.117775111216.453107EURNaNNaN1.10094106.229552
2Research ScientistLeadNoneMediumGermany0EUR19136071.842899320612735152012.842899167214.127189EURNaNNaN1.100167214.127189
3Software EngrLeadFull-timeLargeIndia50INR7141850.905335959411158162602.90533519512.348640USDNaNNaN1.000162602.905335
4Software EngrLeadInternLargeGermany100INR10121841.1632266796806129443.1632261553.317959INRNaNNaN0.0121553.317959
5Data AnalystMidContractLargeUK0EUR1458008.48567464192246286889.48567495578.434242EURNaNNaN1.10095578.434242
6Dt ScientistLeadInternSmallCanada0EUR15126109.48212450741660132843.482124146127.830336INRNaNNaN0.0121594.121785
7DevOps EngineerLeadInternMediumIndia50INR19121069.476205807527588156732.4762051880.789714USDNaNNaN1.000156732.476205
8ML EnginerNoneNoneLargeCanada50GBP1292078.01226159979567107642.012261139934.615939CADNaNNaN0.75080731.509196
9Data AnalystSeniorFull-timeLargeIndia0CAD7105153.99481671722369128239.99481696179.996112INRNaNNaN0.0121538.879938
job_titleexperience_levelemployment_typecompany_sizecompany_locationremote_ratiosalary_currencyyears_experiencebase_salarybonusstock_optionstotal_salarysalary_in_usdcurrencyeducationskillsconversion_rateadjusted_total_usd
99990Sofware EngneerMidPart-timeLargeRemote100EUR96.571535e+04702902075437.35285882981.088144GBPNaNNaN1.309.806856e+04
99991Data AnalystNoneNoneSmallUK50EUR22.957128e+0621311403485901.60034694491.760381CADNaNNaN0.752.229969e+06
99992Dt ScientistLeadInternLargeIndia50EUR181.231832e+05224126285151709.207008166880.127709EURNaNNaN1.101.668801e+05
99993DevOps EngineerNoneNoneLargeUK0USD101.314279e+0648861091030937.66585730937.665857CADNaNNaN0.759.975562e+05
99994ML EngrJuniorInternLargeUSA50GBP183.677303e+043392144341608.02733554090.435535USDNaNNaN1.004.160803e+04
99995DevOps EngineerMidFull-timeLargeIndia50EUR147.533198e+04326029331107922.984608118715.283069USDNaNNaN1.001.079230e+05
99996ML EnginerSeniorContractMediumCanada50USD41.064161e+055777477112670.051623112670.051623EURNaNNaN1.101.239371e+05
99997Machine Learning EngrSeniorPart-timeMediumUK50GBP21.033159e+05963824124137077.889785178201.256720USDNaNNaN1.001.370779e+05
99998Research ScientistJuniorInternMediumRemote100CAD143.749326e+0489902772974212.26169355659.196269CADNaNNaN0.755.565920e+04
99999Research ScientistNoneNoneLargeGermany0CAD75.854700e+0467921110576443.99779157332.998343CADNaNNaN0.755.733300e+04

Duplicate rows

Most frequently occurring

job_titleexperience_levelemployment_typecompany_sizecompany_locationremote_ratiosalary_currencyyears_experiencebase_salarybonusstock_optionstotal_salarysalary_in_usdcurrencyconversion_rateadjusted_total_usd# duplicates
520Data AnalystJuniorPart-timeSmallRemote0USD637651.2939957538395749146.29399549146.293995USD1.00049146.2939954
603Data AnalystLeadContractLargeRemote100EUR8117167.137092905114398140616.137092154677.750801USD1.000140616.1370924
817Data AnalystLeadFull-timeSmallRemote0INR16133376.59684822013970147566.5968481770.799162INR0.0121770.7991624
1046Data AnalystLeadPart-timeMediumRemote0USD3142102.586722231012957157369.586722157369.586722USD1.000157369.5867224
1107Data AnalystLeadPart-timeSmallUK0INR2135164.875684407623569162809.8756841953.718508GBP1.300211652.8383894
1447Data AnalystMidInternLargeUK100GBP1185161.307069385419045108060.307069140478.399189EUR1.100118866.3377764
1521Data AnalystMidInternSmallGermany50INR1167103.5316891896825877257.531689927.090380GBP1.300100434.7911964
1817Data AnalystSeniorFull-timeLargeCanada0INR196240.369622610517757120102.3696221441.228435CAD0.75090076.7772174
2062Data AnalystSeniorInternSmallUK100CAD6100907.380555317118496122574.38055591930.785416GBP1.300159346.6947214
2201Data AnalystSeniorPart-timeSmallUSA50EUR1495882.074292708310350113315.074292124646.581721USD1.000113315.0742924